A particle swarm optimization approach to the nonlinear resource allocation problem

The resource allocation problem seeks to find an optimal allocation of a limited amount of resource to a number of activities for optimizing the objective under the resource constraint. Most existing methods use mathematical programming techniques, but they may fail to derive exact solutions for large-sized problems with reasonable time. An alternative is to use meta-heuristic algorithms for obtaining approximate solutions. This paper presents a particle swarm optimization (PSO) algorithm for conquering the nonlinear resource allocation problem. To ensure the resource constraint is satisfied, we propose adaptive resource bounds for guiding the search. The experimental results manifest that the proposed method is more effective and efficient than a genetic algorithm. The convergence behavior of the proposed method is analyzed by observing the variations of particle entropy. Finally, a worst-case analysis is conducted to provide a reliable performance guarantee.